Importing data into R
#disable scientific notation, so that actual decimal values are imported instead of exponential factors
options(scipen = 999)
# Importing country Metadta dataset into R
country_metadata_dataset <- read_excel("GDP.xls", col_names = TRUE, sheet = "Metadata - Countries")
# Importing GDP (1995-2018) by country dataset into R
gdp_dataset <- read_excel("GDP.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
# Importing GDP percapita (1995-2018) by country dataset into R
gdp_percapita_dataset <- read_excel("GDP per Capita.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
# Importing Manufacturing GDP (1995-2018) percentage by country dataset into R
gdp_manufacturing_dataset <- read_excel("Manufacturing.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
# Importing Agriculture GDP (1995-2018) percentage by country dataset into R
gdp_agriculture_dataset <- read_excel("Agriculture.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
# Importing Service GDP (1995-2018) percentage by country dataset into R
gdp_service_dataset <- read_excel("Service.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
# Importing Industries GDP (1995-2018) percentage by country dataset into R
gdp_industries_dataset <- read_excel("Industries.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
# Importing Ores_Metals_Minerals GDP (1995-2018) percentage by country dataset into R
gdp_ores_metals_minerals_dataset <- read_excel("Ores_Metals_Minerals.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
data.frame(., stringsAsFactors = F) %>%
select(., 1,2,3,40:63)
Income groups Charts
column
Number of Incomegroups
NIG <- length(unique(incomegroup_df[["TableName"]]))
valueBox(NIG, color = "primary")
13
Column
GDP
ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `GDP`)) +
geom_line(aes(y = `GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "GDP by income groups in millions")
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_path).

GDP Percapita
ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `GDP Percapita`)) +
geom_line(aes(y = `GDP Percapita`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "GDP Percapita by income groups")

Industry Percent of GDP
ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Industry Percent of GDP`)) +
geom_line(aes(y = `Industry Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Industry Percent of GDP by income groups")
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_path).

Services Percent of GDP
ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Services Percent of GDP`)) +
geom_line(aes(y = `Services Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Services Percent of GDP by income groups")
## Warning: Removed 38 rows containing missing values (geom_point).
## Warning: Removed 38 rows containing missing values (geom_path).

Agriculture Percent of GDP
ggplot(incomegroup_df, aes(x=year, colour=TableName, group = TableName)) +
geom_point(aes(y = `Agriculture Percent of GDP`)) +
geom_line(aes(y = `Agriculture Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Agriculture Percent of GDP by income groups")
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_path).

Manufacturing Percent of GDP
## Warning: Removed 52 rows containing missing values (geom_point).
## Warning: Removed 49 rows containing missing values (geom_path).

ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Ores_Metals_Minerals Percent of GDP`)) +
geom_line(aes(y = `Ores_Metals_Minerals Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Ores_Metals_Minerals Percent of GDP by income groups")
## Warning: Removed 84 rows containing missing values (geom_point).
## Warning: Removed 77 rows containing missing values (geom_path).

Economy by region Charts
column
Number of Regions
NIG <- length(unique(economy_by_region_df[["TableName"]]))
valueBox(NIG, color = "primary")
7
Service based economy
valueBox("North America", color = "info")
North America
### Manufacturing based economy
valueBox("East Asia and Pacific", color = "info")
East Asia and Pacific
### Agriculture based economy
valueBox("South Asia", color = "info")
South Asia
Column
### GDP
ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `GDP`)) +
geom_line(aes(y = `GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "GDP by Region in millions")

### GDP Percapita
ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `GDP Percapita`)) +
geom_line(aes(y = `GDP Percapita`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "GDP Percapita by Region")

### Services Percent of GDP
ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Services Percent of GDP`)) +
geom_line(aes(y = `Services Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Services Percent of GDP by Region")
## Warning: Removed 14 rows containing missing values (geom_point).
## Warning: Removed 14 rows containing missing values (geom_path).

### Agriculture Percent of GDP
ggplot(economy_by_region_df, aes(x=year, colour=TableName, group = TableName)) +
geom_point(aes(y = `Agriculture Percent of GDP`)) +
geom_line(aes(y = `Agriculture Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Agriculture Percent of GDP by Region")
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_path).

### Manufacturing Percent of GDP
ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Manufacturing Percent of GDP`)) +
geom_line(aes(y = `Manufacturing Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Manufacturing Percent of GDP by Region")
## Warning: Removed 14 rows containing missing values (geom_point).
## Warning: Removed 14 rows containing missing values (geom_path).

### Ores_Metals_minerals Percent of GDP
ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Ores_Metals_Minerals Percent of GDP`)) +
geom_line(aes(y = `Ores_Metals_Minerals Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Ores_Metals_minerals Percent of GDP by Region")
## Warning: Removed 12 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_path).

Knowledge vs traditinoal GDP
knowledge_traditinoal_SD_1to25percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` <= 25) %>%
arrange(TableName, year)
knowledge_traditinoal_SD_25to35percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` > 25 & `Country SD GDP in percent` <=35) %>%
arrange(TableName, year)
knowledge_traditinoal_SD_35to45percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` > 35 & `Country SD GDP in percent` <=45) %>%
arrange(TableName, year)
knowledge_traditinoal_SD_45to55percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` > 45 & `Country SD GDP in percent` <=55) %>%
arrange(TableName, year)
knowledge_traditinoal_SD_55to65percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` > 55 & `Country SD GDP in percent` <=65) %>%
arrange(TableName, year)
knowledge_traditinoal_SD_65to75percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` > 65 & `Country SD GDP in percent` <=75) %>%
arrange(TableName, year)
knowledge_traditinoal_SD_75to100percent_dF <- knowledge_traditinoal_dF %>%
filter(.,`Country SD GDP in percent` > 75) %>%
arrange(TableName, year)
SD_Groups <- c("knowledge_traditinoal_SD_1to25percent_dF","knowledge_traditinoal_SD_25to35percent_dF","knowledge_traditinoal_SD_35to45percent_dF","knowledge_traditinoal_SD_45to55percent_dF","knowledge_traditinoal_SD_55to65percent_dF","knowledge_traditinoal_SD_65to75percent_dF","knowledge_traditinoal_SD_75to100percent_dF")
column
Number of SD percent groups
NSDG <- length(SD_Groups)
valueBox(NSDG, color = "info")
7
### Recessions
valueBox("2001, 2008-2009", color = "info")
2001, 2008-2009
column
World chart
ggplot(knowledge_traditinoal_dF, aes(x=factor(year), group = 1)) +
geom_point(aes(y = `World Mean Knowledge GDP percent`)) +
geom_line(aes(y = `World Mean Knowledge GDP percent`, colour = "1")) +
geom_point(aes(y = `World Mean Traditinoal GDP percent`)) +
geom_line(aes(y = `World Mean Traditinoal GDP percent`, colour = "2")) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "World knowledge based GDP percent mean") +
xlab("year") +
ylab("World mean knowledge based GDP percent") +
scale_color_discrete(name = "GDP category", labels = c("World Mean Knowledge GDP percent", "World Mean Traditinoal GDP percent"))

### SD upto 25 percent
ggplot(knowledge_traditinoal_SD_1to25percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.position = "bottom", legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 213 rows containing missing values (geom_point).
## Warning: Removed 213 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_1to25percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 163 rows containing missing values (geom_point).
## Warning: Removed 162 rows containing missing values (geom_path).

### SD >25 and upto 35 percent
ggplot(knowledge_traditinoal_SD_25to35percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 124 rows containing missing values (geom_point).
## Warning: Removed 124 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_25to35percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 95 rows containing missing values (geom_point).
## Warning: Removed 95 rows containing missing values (geom_path).

### SD >35 upto 45 percent
ggplot(knowledge_traditinoal_SD_35to45percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 145 rows containing missing values (geom_point).
## Warning: Removed 142 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_35to45percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 98 rows containing missing values (geom_point).
## Warning: Removed 95 rows containing missing values (geom_path).

### SD >45 upto 55 percent
ggplot(knowledge_traditinoal_SD_45to55percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 79 rows containing missing values (geom_point).
## Warning: Removed 79 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_45to55percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 48 rows containing missing values (geom_point).
## Warning: Removed 42 rows containing missing values (geom_path).

### SD >55 upto 65 percent
ggplot(knowledge_traditinoal_SD_55to65percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 61 rows containing missing values (geom_point).
## Warning: Removed 51 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_55to65percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 33 rows containing missing values (geom_point).
## Warning: Removed 29 rows containing missing values (geom_path).

### SD >65 upto 75 percent
ggplot(knowledge_traditinoal_SD_65to75percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 14 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_65to75percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 12 rows containing missing values (geom_path).

### SD >75 percent
ggplot(knowledge_traditinoal_SD_75to100percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Knowledge based Percent of GDP`)) +
geom_line(aes(y = `Knowledge based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Knowledge based Percent of GDP by income groups in millions") +
theme(legend.position = "bottom", legend.text = element_text(size=8), legend.margin = margin(t = 0, unit='cm'))
## Warning: Removed 36 rows containing missing values (geom_point).
## Warning: Removed 36 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_75to100percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
geom_point(aes(y = `Traditinoal based Percent of GDP`)) +
geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
theme(axis.text.x = element_text(size=10, angle=90)) +
theme(axis.text.y = element_text(size=10, angle=90)) +
labs(title = "Traditional based Percent of GDP by income groups in millions") +
theme(legend.position = "none")
## Warning: Removed 22 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing missing values (geom_path).
